B. Adcock, C. Anders, and . Hansen, Generalized Sampling and Infinite-Dimensional Compressed Sensing, Foundations of Computational Mathematics, vol.50, issue.6, pp.1-61, 2015.
DOI : 10.1007/s10208-015-9276-6

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.308.8665

B. Adcock, A. C. Hansen, C. Poon, and B. Roman, BREAKING THE COHERENCE BARRIER: A NEW THEORY FOR COMPRESSED SENSING, Forum of Mathematics, Sigma, vol.94840, 2013.
DOI : 10.1017/S0962492900002816

B. Adcock, C. Anders, B. Hansen, and . Roman, A note on compressed sensing of structured sparse wavelet coefficients from subsampled fourier measurements. arXiv preprint arXiv:1403, 2014.

B. Adcock, A. C. Hansen, and B. Roman, The Quest for Optimal Sampling: Computationally Efficient, Structure-Exploiting Measurements for Compressed Sensing, Compressed Sensing and its Applications, 2014.
DOI : 10.1007/978-3-319-16042-9_5

J. Bigot, C. Boyer, and P. Weiss, An Analysis of Block Sampling Strategies in Compressed Sensing, IEEE Transactions on Information Theory, vol.62, issue.4, 2014.
DOI : 10.1109/TIT.2016.2524628

URL : https://hal.archives-ouvertes.fr/hal-00823711

G. Richard, V. Baraniuk, . Cevher, F. Marco, C. Duarte et al., Modelbased compressive sensing. Information Theory, IEEE Transactions on, vol.56, issue.4, pp.1982-2001, 2010.

A. Bastounis, C. Anders, and . Hansen, On the absence of the rip in realworld applications of compressed sensing and the rip in levels, 2014.

F. Bach, R. Jenatton, J. Mairal, and G. Obozinski, Optimization with sparsity-inducing penalties. Foundations and Trends, Machine Learning, pp.1-106, 2012.
DOI : 10.1561/2200000015

URL : https://hal.archives-ouvertes.fr/hal-00613125

[. Chauffert, P. Ciuciu, J. Kahn, and P. Weiss, Variable density sampling with continous sampling trajectories, SIAM Journal on Imaging Sciences, 2014.
DOI : 10.1137/130946642

P. [. Chauffert, P. Ciuciu, and . Weiss, Variable density compressed sensing in MRI. Theoretical vs heuristic sampling strategies, 2013 IEEE 10th International Symposium on Biomedical Imaging, 2013.
DOI : 10.1109/ISBI.2013.6556471

URL : https://hal.archives-ouvertes.fr/hal-00848271

E. Candès and Y. Plan, A probabilistic and ripless theory of compressed sensing. Information Theory, IEEE Transactions on, vol.57, issue.11, pp.7235-7254, 2011.

E. Candès, J. Romberg, and T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information, IEEE Transactions on Information Theory, vol.52, issue.2, pp.489-509, 2006.
DOI : 10.1109/TIT.2005.862083

J. Candès, T. Romberg, and . Tao, Stable signal recovery from incomplete and inaccurate measurements, Communications on Pure and Applied Mathematics, vol.7, issue.8, pp.1207-1223, 2006.
DOI : 10.1002/cpa.20124

T. Candès and . Tao, Near-optimal signal recovery from random projections: Universal encoding strategies? Information Theory, IEEE Transactions on, vol.52, issue.12, pp.5406-5425, 2006.

[. Chauffert, P. Weiss, J. Kahn, and P. Ciuciu, Gradient waveform design for variable density sampling in magnetic resonance imaging, IEEE Transactions on Medical Imaging, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01095320

F. Marco, Y. C. Duarte, and . Eldar, Structured compressed sensing: From theory to applications, Signal Processing IEEE Transactions on, vol.59, issue.9, pp.4053-4085, 2011.

D. Donoho, Compressed sensing. Information Theory, IEEE Transactions on, vol.52, issue.4, pp.1289-1306, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00369486

C. Yonina, M. Eldar, and . Mishali, Robust recovery of signals from a structured union of subspaces. Information Theory, IEEE Transactions on, vol.55, issue.11, pp.5302-5316, 2009.

W. Feller, An introduction to probability theory and its applications, 2008.

S. Foucart and H. Rauhut, A mathematical introduction to compressive sensing, 2013.
DOI : 10.1007/978-0-8176-4948-7

R. Gribonval and M. Nielsen, Beyond sparsity: Recovering structured representations by {\ ell}?1ell}?1 minimization and greedy algorithms Advances in computational mathematics, pp.23-41, 2008.

[. Gross, Recovering low-rank matrices from few coefficients in any basis. Information Theory, IEEE Transactions on, vol.57, issue.3, pp.1548-1566, 2011.
DOI : 10.1109/tit.2011.2104999

URL : http://arxiv.org/abs/0910.1879

[. Gröchenig, J. Luis-romero, J. Unnikrishnan, and M. Vetterli, On minimal trajectories for mobile sampling of bandlimited fields, Applied and Computational Harmonic Analysis, vol.39, issue.3, 2014.
DOI : 10.1016/j.acha.2014.11.002

C. Herzet, C. Soussen, J. Idier, and R. Gribonval, Exact recovery conditions for sparse representations with partial support information. Information Theory, IEEE Transactions on, vol.59, issue.11, pp.7509-7524, 2013.
DOI : 10.1109/tit.2013.2278179

URL : https://hal.archives-ouvertes.fr/hal-00907646

F. Krahmer and R. Ward, Stable and Robust Sampling Strategies for Compressive Imaging, IEEE Transactions on Image Processing, vol.23, issue.2, pp.612-622, 2014.
DOI : 10.1109/TIP.2013.2288004

URL : http://arxiv.org/abs/1210.2380

M. Lustig, D. Donoho, and J. M. Pauly, Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic resonance in medicine, pp.1182-1195, 2007.

R. Leary, Z. Saghi, A. Paul, . Midgley, J. Daniel et al., Compressed sensing electron tomography, Ultramicroscopy, vol.131, pp.70-91, 2013.
DOI : 10.1016/j.ultramic.2013.03.019

C. Adam, M. F. Polak, D. L. Duarte, and . Goeckel, Performance bounds for grouped incoherent measurements in compressive sensing, 2015.

G. Puy, J. P. Marques, R. Gruetter, J. Thiran, D. Van-de-ville et al., Spread Spectrum Magnetic Resonance Imaging, IEEE Transactions on Medical Imaging, vol.31, issue.3, pp.31586-598, 2012.
DOI : 10.1109/TMI.2011.2173698

. Psv09-]-xiaochuan-pan, Y. Emil, M. Sidky, and . Vannier, Why do commercial ct scanners still employ traditional, filtered back-projection for image reconstruction? Inverse problems, p.123009, 2009.

G. Puy, P. Vandergheynst, and Y. Wiaux, On Variable Density Compressive Sampling, IEEE Signal Processing Letters, vol.18, issue.10, pp.595-598, 2011.
DOI : 10.1109/LSP.2011.2163712

URL : http://arxiv.org/abs/1109.6202

H. Rauhut, Compressive sensing and structured random matrices. Theoretical foundations and numerical methods for sparse recovery, pp.1-92, 2010.

B. Roman, A. Hansen, and B. Adcock, On asymptotic structure in compressed sensing. arXiv preprint, 2014.

G. Tauböck and F. Hlawatsch, A compressed sensing technique for OFDM channel estimation in mobile environments: Exploiting channel sparsity for reducing pilots, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.2885-2888, 2008.
DOI : 10.1109/ICASSP.2008.4518252

A. Joel and . Tropp, Just relax: Convex programming methods for identifying sparse signals in noise. Information Theory, IEEE Transactions on, vol.52, issue.3, pp.1030-1051, 2006.

J. A. Tropp, User-Friendly Tail Bounds for Sums of Random Matrices, Foundations of Computational Mathematics, vol.16, issue.2, pp.389-434, 2012.
DOI : 10.1007/s10208-011-9099-z

]. J. Uv13a, M. Unnikrishnan, and . Vetterli, Sampling and reconstruction of spatial fields using mobile sensors, UV13b] Jayakrishnan Unnikrishnan and Martin Vetterli. Sampling high-dimensional bandlimited fields on low-dimensional manifolds. Information Theory, pp.2328-23402103, 2013.

Y. Wiaux, L. Jacques, G. Puy, A. M. Scaife, and P. Vandergheynst, Compressed sensing imaging techniques for radio interferometry, Monthly Notices of the Royal Astronomical Society, vol.395, issue.3, pp.1733-1742, 2009.
DOI : 10.1111/j.1365-2966.2009.14665.x

URL : https://hal.archives-ouvertes.fr/inria-00369428